The magnitude and sign of soil moisture–precipitation coupling (SMPC) is
investigated using a probability-based approach and 10 years of daily
microwave satellite data across North Africa at a 1∘ horizontal scale.
Specifically, the co-existence and co-variability of spatial (i.e. using soil
moisture gradients) and temporal (i.e. using soil moisture anomaly) soil
moisture effects on afternoon rainfall is explored. The analysis shows that
in the semi-arid environment of the Sahel, the negative spatial and the
negative temporal coupling relationships do not only co-exist, but are also
dependent on one another. Hence, if afternoon rain falls over temporally
drier soils, it is likely to be surrounded by a wetter environment. Two
regions are identified as SMPC “hot spots”. These are the south-western
part of the domain (7–15∘ N, 10∘ W–7∘ E), with
the most robust negative SMPC signal, and the South Sudanese region
(5–13∘ N, 24–34∘ E). The sign and significance of the
coupling in the latter region is found to be largely modulated by the
presence of wetlands and is susceptible to the number of long-lived
propagating convective systems. The presence of wetlands and an irrigated
land area is found to account for about 30 % of strong and significant
spatial SMPC in the North African domain. This study provides the first insight
into regional variability of SMPC in North Africa, and supports the potential
relevance of mechanisms associated with enhanced sensible heat flux and
mesoscale variability in surface soil moisture for deep convection
development.

Soil moisture can affect the state of the lower atmosphere through its impact
on evapotranspiration and surface energy flux partitioning
(Eltahir, 1998; Klüpfel et al., 2011). Especially in the “hot spots” of
soil moisture–precipitation coupling (SMPC), like the semi-arid Sahel
(Koster et al., 2004; Miralles et al., 2012; Taylor et al., 2012), soil moisture exerts strong
control on evapotranspiration (Dirmeyer, 2011; Lohou et al., 2014; Timouk et al., 2009), influencing the development of the daytime planetary boundary
layer (BL), and hence convective initiation and precipitation variability.
Most of the physical understanding of how soil moisture could alter BL
properties and affect development of convection comes from 1-D to 3-D model
analyses (Nicholson, 2015; Seneviratne et al., 2010). Observational evidence of
the SMPC largely relies on the measurements of recent field campaigns
(Goutorbe et al., 1994; Redelsperger et al., 2006), and
hence is often limited to a short spatio-temporal scale. Such observational
analyses present unique evidence of environmental conditions preceding
convection development (Lothon et al., 2011) and can be further used as
a testing ground to evaluate and improve the physical parametrizations of models
(Couvreux et al., 2013). Both observational and modelling studies
agree reasonably well on the effect of soil moisture availability and
heterogeneity on the lower atmospheric stability (Kohler et al., 2010)
and convective initiation at the mesoscale (Birch et al., 2013; Taylor, 2010). However, the impact of soil moisture
on convective precipitation remains more uncertain. At the mesoscale, there is a
disagreement in the sign of the SMPC between observations and models which use
parametrizations of deep convection (Hohenegger et al., 2009; Taylor et al., 2012, 2013). Recent satellite-based analysis has demonstrated
that the choice of soil moisture parameter itself (temporal anomaly or
spatial gradient) and related differences in physical mechanisms have a
direct effect on the resulting sign of the coupling. Guillod et al. (2015)
found a positive temporal (i.e. using the soil moisture anomaly) but negative
spatial (i.e. using the spatial soil moisture gradient) SMPC over most of the
globe at a 5∘ horizontal scale. Our study addresses the question of
co-existence of spatial and temporal SMPC on a finer
1∘× 1∘ horizontal grid. Specifically, we use
10-year satellite records of daily soil moisture from the AMSR-E and 3-hourly
TMPA precipitation product to investigate spatio-temporal co-variability of
observed coupling relationships in the region of North Africa.

Both modelling and observational studies reported the possibility of negative
as well as positive SMPC (Nicholson, 2015).
Spatial gradients in soil moisture can affect BL state and convection
initiation through thermally induced mesoscale circulations on 10 to 100 km
scales (Rochetin et al., 2017; Taylor and Ellis, 2006; Taylor et al., 2007). In association with this
mechanism and under favourable thermodynamic conditions, convection is likely
to be initiated over spatially drier soils, indicating a negative SMPC
(Garcia-Carreras et al., 2011; Taylor et al., 2011). However, whether the further
development and propagation of moist convection will occur over drier or
wetter soils remains less clear. The modelling study of
Froidevaux et al. (2014) suggested that negative SMPC is possible under very
weak surface wind conditions, and is associated with stationarity of convective
systems once initiated. The opposite sign is expected under a stronger
horizontal advection, which will support propagation of the developing moist
convection downwind, i.e. from dry to wet soils, and its further
amplification over wetter areas. Another important factor is related to the
life cycle of mesoscale convective systems (MCSs) and thus their organization
in space and time (Mathon et al., 2002). Small-scale convective systems are
expected to be particularly sensitive to surface moisture variability and
will propagate preferentially towards spatially drier soil, bounded by
wetter surroundings (Taylor and Ellis, 2006). Alternatively, larger organized
systems have been found to evolve towards wetter soils – areas of increased
latent heat flux, convective available potential energy (CAPE) and moist
static energy (MSE) (Clark et al., 2003; Taylor and Lebel, 1998). Hence, these systems are
expected to be more sensitive to soil moisture availability.

The impact of temporal anomalies of soil moisture on the atmospheric BL and
moist convection is largely governed by thermodynamical processes, and may
likewise result in a coupling of both signs. Wet soils are expected to lead
to an increase of boundary layer MSE or similarly equivalent potential
temperature, through a decreased boundary layer height and subsequent
less vigorous entrainment (Alonge et al., 2007; Eltahir, 1998). The
enhanced MSE over wet soils is favourable for convective rainfall formation.
Dry soils, on the contrary, are associated with a reduced MSE and thus
provide lower potential for convection development and may even suppress
the existing MCS (Gantner and Kalthoff, 2010) or deviate its propagation direction
(Wolters, 2010). However, modelling and observational evidence indicates
that both dry and wet soils can favour moist convection,
depending on the morning stratification of the lower atmosphere (Findell and Eltahir, 2003a, b)
into which the daytime convective BL is growing (Couvreux et al., 2012; Ek and Mahrt, 1994; van Heerwaarden and Guerau de Arellano, 2008).

The relevance of mesoscale spatial heterogeneity of soil moisture in
favouring moist convection over wet and dry temporal soil moisture anomalies
was demonstrated by e.g. Clark et al. (2003) and Taylor and Ellis (2006)
respectively. However, until recently no attempts had been made to directly
compare the temporal and spatial aspects. The first comparison of the spatial
and temporal effects of soil moisture on precipitation was presented by the
study of Guillod et al. (2015) (hereafter, G15) at a 5∘ horizontal scale.
Applying the probability-based approach of Taylor et al. (2012) (hereafter,
T12) to 10 years of global satellite-based soil moisture and precipitation
data, they demonstrated that a negative spatial (rain over spatially drier
soils) and a positive temporal (rain over temporally wetter soils) SMPC
dominate over most of the globe and are not mutually exclusive of one another. G15 suggested
that the two effects might be interconnected through spatial coupling
mechanisms, in which adjacent precipitation would provide required moisture
to enhance convection development over spatially but not temporally drier
soil. Using multiple data sets, G15 showed that the signal is robust across
different input data sets. However, in a few regions, including the Sahel in
Africa, an opposite temporal relationship was revealed: spatially and
temporally negative coupling was found to co-exist in contrast to the
global relationship.

In this study, we further explore spatial and temporal SMPC as well as their
co-existence in the North African region using the T12 method on a finer
1∘× 1∘ horizontal grid. Furthermore, we provide
insight into the regional co-variability of the spatial and temporal effects on
afternoon rainfall. The analysis is realized following two main steps.

Identification of the factors that influence the magnitude and variability of the spatial SMPC measure. By doing so we address the following question: which physical processes
likely underlie the observed spatial SMPC relationship, if any?

Analysis of the link between the spatial and temporal effects of soil moisture on precipitation.
This part addresses the two following questions: are spatial and temporal negative coupling relationships independent, and if not, how do they inter-relate?

We reproduce and apply the probability-based approach of T12 to 10 years of
daily AMSR-E soil moisture and 3-hourly TMPA precipitation records. In
contrast to the previous studies, we estimate the temporal and spatial
coupling effects at a finer 1∘ horizontal scale, which reveals
previously hidden smaller scale effects of land cover features on the SMPC
relationship.

The first part of the study (Sect. 4) includes an analysis of the regional variability and robustness of the observed spatial SMPC at
varying horizontal scales i.e. from the original 5 to 2.5∘
and 1∘. Identification of the factors relevant for the observed spatial SMPC
distribution includes a sensitivity analysis of the spatial coupling measure
to the presence of soil moisture parameter extremes (Sect. 4.3) and to the
MCS life cycle
(Sect. 4.4). The link between the temporal and spatial SMPC is assessed using correlation
analysis in Sect. 5.1. The summary and discussion in Sect. 6 is delivered in two
parts. Section 6.1 provides main conclusions on the potential physical
mechanisms which likely underlie the observed coupling relationships.
Section 6.2 reviews the reasons behind the opposite sign of the temporal
coupling identified in the North African region as compared to the dominantly
positive relationship identified in G15 over the globe. The paper concludes
with more general discussion of the SMPC hot spots and conclusions in
Sect. 7.

2.1 Study domain

We focus our analyses on the North African region (5–20∘ N,
20∘ W–40∘ E) (Fig. 1, dashed rectangle) during
the summer period (JJAS). This region has been repeatedly pointed out as a
hot spot of land–atmosphere interactions (Dirmeyer, 2011; Gallego-Elvira and Taylor, 2016; Miralles et al., 2012), and one of the most vulnerable regions with
respect to climate change (Dirmeyer and Wang, 2014; Dirmeyer et al., 2012). A major feature
affecting the Sahelian climate is the West African monsoon
(Janicot and Thorncroft, 2008), which is associated with high precipitation
variability (Nicholson, 2013). During the monsoon, soil moisture
fluctuations are strongly influenced by precipitation at a large range of
spatial and temporal scales. Atmospheric and surface fields display strong
meridional gradients between 10 and 20∘ N
(Fig. 1, zonal plot), shaped by the migration of the summer time
rain belt, also referred to as the intertropical convergence zone (ITCZ). Wind
convergence at the surface is observed further north, around
18–20∘ N, along the intertropical discontinuity (ITD), where the
cool and moist monsoon flow meets hot and dry Saharan air. Associated with
the meridional heat gradient, the monsoon circulation and related large-scale
structures like the African easterly jet (AEJ), as well as synoptic
disturbances like the African easterly waves (AEWs), largely modulate
convection activity over the region (Duvel, 1990; Mohr and Thorncroft, 2006).
Additionally, evidence supporting a significant role of the surface state in
the triggering of deep precipitating convection is steadily growing
(Nicholson, 2015).

The conditions in mid-July to August may be less favourable for a strong surface
influence on convection. Compared to the drier early and late monsoon months of
June and September, the wetter period – from July through August – is characterized
by a typically lower level of free convection (LFC) (Guichard et al., 2009; Taylor et al., 2011)
and less pronounced spatial contrast between fluxes due to more dense vegetation
(Kohler et al., 2010; Lohou et al., 2014). In our study, the role of the monsoon dynamics is
not addressed to preserve maximum sample size for the sake of statistical significance.

We intentionally extend our analysis further eastwards. Despite the inherent
zonal symmetry of surface and atmospheric parameters (as in precipitation in
Fig. 1), considerable differences exist in rainfall and large-scale
circulation regimes between east and west. Distinct orography, intensity of
surface and upper level jets and wave disturbances are likely to bring
dissimilarities in the sensitivity of convection to the surface state between
the two regions. The eastern African domain can also remotely influence
convection in the western part of the region via the genesis of westward-propagating AEWs (Laing and Carbone, 2008) and long-lived MCSs (Laing et al., 2012).
However, notably few studies have investigated land–atmosphere interactions in
the eastern Sahel.

2.2 AMSR-E soil moisture

Soil moisture (SM) data from the Advanced Microwave Scanning Radiometer –
Earth Observing System (AMSR-E, June 2002–October 2011) are analysed in this
study. The AMSR-E unit is carried on board the polar orbiting AQUA
satellite, measuring brightness temperatures in 12 channels, at 6 different
frequencies (6.9–89 GHz) (Njoku and Jackson, 2003). Soil moisture derived from the
lowest C-band frequency of 6.9 GHz is used here, as lower frequencies
experience less signal contamination from vegetation and surface roughness,
and are able to receive emission information from deeper soil layers
(Owe et al., 2008). The AQUA orbit is
sun-synchronous, with typically one overpass per pixel per day at either 13:30
or 01:30 local solar time (LST). In order to capture the surface moisture
state shortly before afternoon convection activity, only data of ascending
day orbit, i.e. 13:30 LST, are used here. It is important to note that the
day overpass is prone to higher biases compared to the night overpass
because of the greater temperature differences between the surface and canopy
involved in the physics algorithm (Njoku and Jackson, 2003).

We utilize the Level 3 estimates of AMSR-E soil moisture derived with the
Land Parameter Model (Owe et al., 2008) for JJAS 2002–2011.
The product is available at a 0.25∘× 0.25∘ spatial
resolution. The LPRM is not valid for dense vegetation and water bodies.
Therefore pixels with an optical depth > 0.8 are excluded. Water body and
soil moisture quality masks were adopted from the materials of T12.
Accordingly, pixels containing more than 5 % water are excluded, using
water body classification of the 1 km Global Land Cover 2000 data set
(available online at
http://forobs.jrc.ec.europa.eu/products/glc2000/products.php,
last access: September 2017). Application of the soil moisture quality mask, based
on the correlation analysis between precipitation and soil moisture data
sets, is intended to reduce the number of pixels covered with wetlands (for
details, see the supplement in T12).

Many days (40–50 %) do not contain soil moisture information due to
satellite revisit times. Over a given longitude per day the number of
overpasses below 40∘ N does not exceed one, with occasionally daily sampling or
sampling every third day (Njoku and Jackson, 2003). The latter
significantly reduces the size of the collected rainfall event sample
available for the analyses. The AMSR-E instrument is chosen because it
documents a relatively long period and performs better than ASCAT
(Dorigo et al., 2010) over sparsely vegetated and deserted areas. The AMSR-E
product also proved to be accurate at the precipitation event scale in
capturing rain-related soil moisture variability and timing, when compared
with in situ data in the Sahel (Gruhier and Rosnay, 2008).

In this study we utilize the product version 7 (TRMM-3B42), which includes
several modifications to the algorithm and additional satellite data
(Huffman and Bolvin, 2014). Consistent with the soil moisture record, only 10 years
(2002–2011) of JJAS precipitation data are used. To ensure similar solar
forcing on the surface and boundary layer, the 3 h precipitation time series
for the present application are adjusted to LST (based on longitude) by
taking the closest 3 h UTC time step. It is important to note that any 3 h
TMPA value does not refer directly to its nominal hour, but it represents the
average of the “best” overpass data within a 3-hourly window, centred
around the nominal hour, i.e. ±90 min range. Variable time of the TMPA best data average is
not expected to significantly affect our SMPC results.

3.1 Description of statistical framework

The SMPC in this study is referred to as the relationship between the
afternoon convective rainfall and the antecedent soil moisture conditions.
Using the method of T12, we examine first whether afternoon rainfall is more
likely over soils that are untypically (relative to its climatology) drier or
wetter than their surroundings. Next, following the definition of G15, we
assess whether afternoon rainfall is more likely on days when soils are
untypically drier or wetter than their temporal mean. Subsequently, the
probability of convective rainfall events to occur over spatially
drier or wetter soils being higher than expected is referred to as spatial SMPC,
while the likelihood of convective rain events to occur
over temporally wetter or drier soils being higher than expected is described by temporal SMPC. The
following paragraph describes criteria which are used to define a convective
precipitation event and evaluate soil moisture statistics antecedent to
every event. The framework algorithm implemented in this study largely
follows the method of T12 and is summarized in Fig. 2.

Figure 2Schematic of data post-processing and statistical framework protocol implemented in the study.

We define a convective event location, Lmax, as the location
where accumulated afternoon
precipitation between 15:00 and 21:00 LST exceeds a threshold of 6 mm.
Then, locations of afternoon accumulated precipitation minima,
Lmin, are identified within a 5 × 5 pixel box
(1.25∘× 1.25∘)1
centred at Lmax (Fig. 2c).
The choice of a later accumulation time than in T12 (i.e. 15:00–21:00 LST
instead of 12:00–21:00 LST) ensures that the soil moisture measurement at
13:30 LST precedes precipitation without introducing additional filters. The afternoon accumulated rainfall threshold
that is twice larger than in T12 yields qualitatively similar results, though it leads to a slightly higher mean SMPC
significance over the domain. According to additional sensitivity tests, the
choice of higher threshold values in the method mostly influences the number
of significant grid boxes linked to a reduction in the event sample size, yet
it does not qualitatively affect the dominant preference of the afternoon
rainfall over specific soil moisture conditions (Petrova, 2017).

The following set of assumptions is used to improve the accuracy of the
convective event sample. If one of the conditions is not fulfilled, an event
is excluded from further calculation.

Accumulated precipitation in the preceding hours (06:00–15:00 LST) in
the entire 1.25∘× 1.25∘ box must be zero.

The elevation height difference within the event box must not exceed 300 m.
This is done to minimize the effect of orographic uplifting on the
rainfall variability. The resulting distribution of the orography mask is
shown in Fig. 3a.

The number of identified Lmin locations within one box must
be three or more (for averaging reasons). In that case, all Lmin locations
will have the same afternoon accumulated precipitation value, which will most
often be zero.

If boxes overlap, the event with larger afternoon accumulated
precipitation value is retained.

Once events are identified, the soil moisture anomaly, S′, measured prior to the
precipitation event (at 13:30 LST) at Lmax,
Lmin‾ or any combination of the two is stored and
analysed. S′Lmin‾ represents an average value of
S′ measured in every identified Lmin location within a
1.25∘× 1.25∘ event box. S′ has its climatological
mean subtracted, calculated as linear averaging within a 21-day moving window
centred on the particular day and across the entire multi-year record. To
exclude the contribution of a rain event from the anomaly values, the year of the
event is excluded from the climatological mean calculation. To estimate
whether soils were anomalously dry or wet in the location of maximum rain
(Lmax) compared to the expected range (climatology) for that
location,
we store the pre-event soil moisture anomaly at Lmax, i.e. Ye:
Se′Lmax (Fig. 2d). To investigate whether soils in the
location of maximum rain (Lmax) were drier than in the neighbour
region(s) where it rained less or did not rain (Lmin),
we calculate the pre-event soil moisture gradient between Lmax
and Lmin‾ scaled per 100 m, i.e. Ye:
Δ(Se′Lmax)=Se′Lmax–Se′Lmin‾ with the
dimension of m3 m−3 100 m−1, where Δ stands for the gradient.

For each Ye parameter we define the control sample Yc,
represented by an array of corresponding Y values measured in the same
Lmax and Lmin event locations in the same calender
month, but on the non-event years. The measure of coupling is then quantified
by the magnitude of the difference between mean statistics of the event and
control samples, δe= mean(Ye)−
mean(Yc), and the measure of δe significance
(Fig. 2e). Significance is represented by a percentile,
Pe, of the observed δe in a bootstrapped sample
of δ values that is observed by chance. For that, Ye and
Yc are pooled together and resampled without replacement 5000
times.

Parameters of δe and Pe calculated for the soil
moisture gradients Δ(Se′Lmax) prior to the
event quantify the preference of rain to occur over soils drier
(δe< 0, Pe≤ 10 %) or wetter
(δe> 0, Pe≥ 90 %) than their
1.25∘× 1.25∘ environment, and are referred to as
negative or positive spatial SMPC respectively. The same parameters
estimated for the temporal soil moisture anomaly
Se′Lmax instead specify the expressed preference of rain
to occur over soils drier or wetter than their temporal mean, i.e. negative or
positive temporal SMPC, accordingly.

In this study, estimation of δe and its significance
Pe for the spatial and temporal coupling is realized over
5∘× 5∘, 2.5∘× 2.5∘ and
1∘× 1∘ boxes. Aggregation of event statistics at a
higher resolution than used in the global studies of T12 and G15 results in a
smaller event sample size per grid box, yet allows a reduction of the
potential influence of meridional or zonal gradient in the parameter
statistics; i.e. it makes the spread in underlying surface and atmospheric
moisture conditions across the box latitudes smaller
(Sect. 4.2). The latter is valuable for the interpretation of
obtained statistics in terms of land cover and atmospheric state. Hence, most
of the study focuses on the smallest 1∘× 1∘ spatial
grid.

3.2 Statistics of convective events

Application of the algorithm to the 10 years of JJAS AMSR-E soil moisture and
TMPA precipitation time series yields 10 131 afternoon rainfall events. The
distribution of identified events over the domain at
5∘× 5∘ and 1∘× 1∘ grid is
shown in Fig. 3b and c respectively. The signature of orography and
large-scale dynamic effects on event occurrence becomes more evident at the
finer event-aggregation scale, thus giving an advantage to the highest
horizontal resolution. Figure 3c shows that most events occur
between 10 and 18∘ N, and the occurrence maxima are zonally aligned.
Two maxima are found over the central Sahel, covering the area between
10∘ W and 15∘ E – aligned with the mean position of the AEJ
core (Fig. 1b). Another two maxima are evident at about 22 and
30∘ E.
Overall, the obtained distribution of identified rain events at 1∘× 1∘
grid resolution is consistent with the observed distribution of intense MCSs over the
region (Mathon and Laurent, 2001).

4.1 SMPC at a 5∘ horizontal scale: comparison with previous studies

We start our assessment by investigating the spatial soil moisture–precipitation coupling relationship. In agreement with the global-scale studies of T12 and G15, we find a
dominantly negative spatial SMPC in the North African domain on the
5∘× 5∘ grid, i.e. a strong preference for
convective rainfall events to occur over spatially drier soils
(Fig. 4a). The majority of the 5∘× 5∘
boxes (72 %) have percentile values Pe lower than 10 %,
implying a significant negative difference in the mean magnitude of soil
moisture gradients Δ(Se′Lmax) prior to the
events relative to their typical (non-event) state. No significant positive
difference between event and non-event conditions is found at the 5∘
scale (Table 1).

Figure 5Percentage of 5∘× 5∘ grid boxes with
significantly negative (Pe< 10 %, in red) and positive
(Pe> 90 %, in blue) spatial SMPC over the North African
domain in this study and previous studies of T12 and G15. Different data set
combinations used in T12 and G15 are listed. Mean and SD of the negative
(positive) SMPC fractions across the experiments are shown as a red (blue)
solid line and shading accordingly. Following visual inspection, the
experiments, in which a significant negative SMPC relationship exists in the
western region of the North African domain, are marked with a
rhomb.

Figure 5 further compares the percentage of the domain area with
significant negative and positive coupling identified in our study and in the previous studies of T12 and
G15 (see also Table A1 in the Appendix). The differences arise due to
disparities in the data sets and methodologies. The weakest negative and the
strongest positive coupling signals correspond to the estimates based on
the PERSIANN (Precipitation Estimation from Remotely Sensed Information using
Artificial Neural Networks; Hsu et al., 1997) data set from G15.
This is possibly linked to the lower consistency between the PERSIANN
precipitation and soil moisture variability in time (T12). On average,
results based on different data set combinations summarized in
Fig. 5 agree that afternoon precipitation occurs more often than
expected over spatially drier soils in 42 % of the
5∘× 5∘ boxes, compared to only 4 % with a
preference over spatially wetter soils (red and blue lines in
Fig. 5 respectively).

The variability of spatial SMPC patterns among different data set combinations
has shown to be quite strong over the globe and was not analysed further in G15. We find,
however, that in the North African domain, areas of significant negative spatial coupling
are fairly consistent. One of the most robust negative spatial SMPC signals is found in the
south-western part of the domain (Fig. 4a, b). A total of 14 out of 18
data set combinations summarized in Fig. 5, including this study,
locate the cluster of the lowest percentiles roughly between
5–15∘ N and 10∘ W–10∘ E (Fig. 5,
rhombs). This region occupies a relatively vast and flat area, associated
with a reduced orographic forcing on convection development compared to the
east, and a regional minimum in cold cloud occurrence (Laing and Carbone, 2008). The
effect of large-scale structures like AEWs and the AEJ on convection, on the
contrary, is expected to be stronger in the western Sahel than further east.
However, this does not exclude and may even favour higher sensitivity of
convection triggering to soil moisture heterogeneities (Adler et al., 2011; Gantner and Kalthoff, 2010). Overall, the identified negative spatial SMPC relationship in the
region is consistent with the recent observation-based (Lothon et al., 2011; Taylor, 2010; Taylor and Ellis, 2006) and model-based (Birch et al., 2013; Gantner and Kalthoff, 2010; Garcia-Carreras et al., 2011; Taylor et al., 2013) studies in the western Sahel.

Another cluster of the lowest percentiles and the largest differences in soil
moisture state between event and non-event days δe is
identified in the south-east of the domain (Fig. 4a, b). The
proximity to the Ethiopian Highlands and the presence of extensive seasonally
flooded regions in this area makes it generally difficult to isolate the effect
of the surface state on convection. This also possibly led to less agreement in
the spatial SMPC estimates identified in our study, G15 analyses and T12 analyses
(not shown). Unlike in the western Sahel, no accurate estimates of the SMPC
exist in this eastern region.

4.2 Robustness of the negative SMPC at finer 2.5 and 1∘ scales

To identify factors and potential physical mechanisms that influence the
magnitude and variability of the SMPC we reduce the event-aggregation scale
to a finer 2.5∘× 2.5∘ and
1∘× 1∘ horizontal grid2. In particular, aggregation of the convective rainfall events and
corresponding soil moisture statistics over the smallest
1∘× 1∘ grid boxes reveal more details on the
effects of land surface conditions on the SMPC.

The percentile maps obtained for the finer scales are presented in Fig. 4c and e.
Despite the reduction in the number of significant δe values,
largely due to the decreased number of events in every box, negative spatial SMPC
relationships remain dominant at the finer scales, and exhibit a similar spatial
pattern as on the 5∘× 5∘ grid. The featured regions of
significant negative coupling now scale down to the territories of Burkina Faso,
Benin, parts of Côte d'Ivoire, Ghana and Mali (7–15∘ N, 10∘ W–7∘ E)
in the west, as well as South Sudan (5–13∘ N, 24–34∘ E) in the east
(Fig. 4e). In total, 42 % (21 %) of the boxes reveal significant negative
difference δe for the 2.5∘ (1∘) scale,
versus an initial 72 % at the 5∘× 5∘ grid
(Table 1).

The overall distribution of the difference δe does not
change at the finer scales (Fig. 4d, f). However, multiple pixels
with a positive δe emerge. For example, a small region
enclosed between the Cameroon mountains and Jos Plateau (7∘ N,
8∘ E; Fig. 4f) now indicates a higher likelihood of
rainfall to occur over spatially wetter soils. The relationship, though
non-significant, is plausible. This area includes part of the Niger River
valley and represents a prominent location of intense convection and a local
maximum of the cold cloud occurrence, linked to the initiation of convection
at the lee side of the high terrain (Laing and Carbone, 2008). The potential link
between the land surface characteristics and SMPC parameter is explored in
more detail in the following section.
In total, 14 % of the 1∘× 1∘ boxes reveal a
positive δe shift, compared to less than 6 and 3 % for
coarser 2.5∘× 2.5∘ and
5∘× 5∘ grids respectively.

Figure 6(a) Schematic box plot illustrating the quantile range
(Q25–1.5 × IQR, Q75+ 1.5 × IQR) used to
identify extreme Δ(Se′Lmax) values. Here,
Q75 and Q25 are the third and first quartiles respectively, and the
interquartile range (IQR) is the difference between them.
(b) Natural wetland fraction from Matthews and Fung (1987) on a
1∘× 1∘ grid (adopted from Fig. 3 in
Prigent et al., 2007). Names of major wetland and river locations are marked
with a number. (c) Distribution of soil moisture gradient
Δ(Se′Lmax) extremes in the corresponding
event sample of a 1∘× 1∘ box (colour).
Δ(Se′Lmax) is considered to be an extreme
if it lies outside the (Q25–1.5 × IQR,
Q75+ 1.5 × IQR) range. If more than one extreme is found
in one grid box, then its average value is shown. Black crosses indicate
boxes containing Lmin locations, in which climatology of daily soil moisture
drying rates does not vary much with different absolute soil moisture values.
This relationship is equivalent to low soil moisture variability in time
and is representative of a wet (flooded) locations. For detailed algorithms,
the reader is referred to Fig A1. The distribution of identified potentially
flooded locations is consistent with the natural wetland
fraction (b).

4.3 Evidence of a “wetland breeze” mechanism in the SMPC statistics

In areas like the Cameroon Mountains, where orography or floodplains have an
effect on deep convection development, persistent wet and dry surface
moisture patterns may pre-exist or develop, and therefore, lead to the
occurrence of spatial soil moisture gradients that are stronger than usual. In the
SMPC statistics, such gradients occur as extremes in a given distribution of
soil moisture gradients Δ(Se′Lmax) within a
1∘× 1∘ box.
Here, we define a gradient Δ(Se′Lmax) to be an
extreme if it lies outside the following range:
(Q25–1.5 × IQR) to (Q75+ 1.5 × IQR), where
Q75 and Q25 are the third and first quartiles respectively, and the
interquartile range (IQR) is the difference between them (Fig. 6a).

The distribution and magnitude of the extreme soil moisture gradients identified
in the domain are shown in Fig. 6 (color shading). We find that 28 % of all valid 1∘× 1∘ boxes
contain extreme Δ(Se′Lmax). Most of the
extreme soil moisture gradients are located in the regions of significant
negative coupling in the west and east. In these areas, extreme
Δ(Se′Lmax) values lead to an overestimation of the
Δ(Se′Lmax) sample mean and therefore,
δe magnitude. As a result, extreme gradients appear to
predefine SMPC significance. Removal of extremes leads to a decrease in the
number of boxes with significant negative spatial coupling by 30 %.
However, in most cases the sign of the coupling remains unchanged (not shown).

Further analysis shows that extremes tend to cluster around major rivers and wetland areas in the east and west (Fig. 6b, c). Regional distribution of extreme Δ(Se′Lmax) is
consistent with the distribution of natural wetland fraction, presented
earlier by Matthews and Fung (1987) and shown in Fig. 6b, and more
recent estimates of inundation regions identified by Prigent et al. (2007).
Strong positive soil moisture gradients are found around the Senegal River
close to the coast, and on the lee side of the Cameroon Mountains. Strong
negative soil moisture gradients are more numerous and seen all along the
western flow of the Niger River, downwind of the permanent wetlands of Ez
Zeraf Game Reserve and irrigated lands of the Gezira Scheme in Sudan. The
scatter of the extremes in the east is likely related to the recurrent floods
of the White Nile River. The reason for the absence of extremes around the
Logone floodplains is obscure.

To verify the link between the location of extreme soil moisture gradients
and flooded areas, we additionally identify 1∘× 1∘
boxes with the events, of which Lmin pixels were likely covered with
a wetland or a floodplain, and hence were wetter than the neighbouring
Lmax location (Fig. 6c, crosses). This is done by
calculating a linear regression function between the climatology of 1-day soil
moisture drying rate (i.e. difference in soil moisture on the event day 0 and
on day −1) and the climatology of initial soil moisture value (i.e. soil
moisture on day −1)3 (Appendix Fig. A1). We consider the Lmin
location to be likely flooded, if (a) all initial soil moisture values are
high, and (b) climatological values of drying rate are always small and do
not vary much with the initial soil moisture (regression slope is close to
zero). For details the reader is referred to Appendix A and Fig. A1.
From Fig. 6c it is seen that the distribution of grid boxes
with the potentially flooded Lmin locations conforms with the distribution
of the natural wetland fraction in Fig. 6b and includes the majority of
extreme Δ(Se′Lmax) locations. This result
supports the presence of the link between the afternoon rainfall maximum and
flooded areas.

The identified sensitivity of the afternoon rainfall to the strong negative
soil moisture gradients around water bodies is in agreement with the results
of the observational-based study of Taylor (2010). Analysing 24 years of
Meteosat brightness temperatures over the Inner Niger Delta, he found that
convection was initiated more often over and to the east of the wetland in
the morning hours. However, later in the day, mesoscale convective systems
tended to develop and propagate away from the wet areas towards drier soils,
suggesting the formation of deep convection and afternoon precipitation over
negative soil moisture gradients. Similarly, observed by Alter et al. (2015),
enhancement of rain to the east of irrigated land at 14∘ N,
33∘ E and its suppression over the Gezira Scheme itself is
consistent with the location of negative (positive) extreme soil moisture
gradients to the west (east) of the irrigated region (Fig. 6c). All
the above supports the consistency of the observed afternoon rainfall
intensification over the drier soils adjacent to flooded areas with
a wetland breeze mechanism. The ability of the method to capture these
effects is also noteworthy.

4.4 Effect of propagation of deep convective events on the SMPC statistics in eastern and western domains

Another physical effect that may influence the SMPC relationship is related
to the propagation and evolution of mesoscale convective systems (not
accounted for by the current algorithm).
Previous studies indicate that an opposite SMPC relationship might be
expected at early versus late stages of MCS development (Alonge et al., 2007; Clark et al., 2003; Gantner and Kalthoff, 2010; Taylor and Ellis, 2006; Taylor and Lebel, 1998; Taylor et al., 2010). In this
respect, a distinct strength or even sign of the spatial SMPC measure may
result from separation of the rainfall events into those formed by weaker
and smaller MCSs – mostly found in the early afternoon – or by long-lived
and organized MCSs – dominant during late afternoon hours
(Mathon and Laurent, 2001).
Differences in the SMPC response to the MCS life cycle are also expected to exist
between the two regions of significant negative coupling, in the east and
west. To characterize these differences, we analyse precipitation diurnal
cycles averaged over event days in the east and west first
(Fig. 7), and then estimate sensitivity of the spatial SMPC to
varying rainfall accumulation times (Fig. 8).

The Hovmöller diagram of rainfall averaged over 1000 event days in the western
domain (black rectangle in Fig. 4) shows that intensification of the
moist convection in the region is generally concentrated around main orographical
features (Fig. 7a, c). The peak in precipitation occurs at similar times
across the domain, and thereby does not reveal the expressed signature of the system
propagation. Most of the MCSs are therefore expected to be shorter lived and smaller,
as their dissipation locations would be found close to their initiation (Mathon and Laurent, 2001).

In the east, on the contrary, the strong south-western propagation component
of moist convection dominates the zonal progression of the most intense
rainfall during the diurnal cycle averaged over 754 event days
(Fig. 7d). A large number of MCSs initiate at the lee side of the
Ethiopian Highlands and propagate westward, undergoing cycles of regeneration
and growing into mature and organized MCSs (Laing and Carbone, 2008; Laing et al., 2012). The
emergence of an absolute rain rate maximum downwind of the permanent wetlands
of the Ez Zeraf Game Reserve (at 30∘ E) during afternoon hours
supports an influence of the flooded areas on moist convection
intensification in the region (Fig. 7d, f). Consistent with the
results of Taylor (2010) obtained for the Niger Inland Delta, the
presence of wetlands in the eastern domain is expected to increase the number
of organized and long-lived propagating MCSs in the late afternoon,
originating from either locally triggered MCSs, i.e. formed at the dry
land–wetland boundary, or from re-intensified pre-existing westward
propagating systems.
We may therefore expect a greater sample of long-lived and organized
propagating MCSs to be found in the late afternoon hours in the Eastern than
in the western domain. Accordingly, the response of the SMPC statistics to
propagating MCSs is expected to be stronger in the east compared to the west.
Figure 8, which shows the change of the SMPC parameter between
different rainfall accumulation time periods, confirms this hypothesis. For
this assessment an additional area in the north is considered (dashed
rectangle in Fig. 4), as representative of a region where
large-scale atmospheric and surface conditions differ from those of the east
and west domains.

Figure 7(a, b) Longitudinal cross-sections of maximum elevation
height in the western and eastern domains respectively, (c, d) diurnal cycles of the rain rate averaged over event days and domain
latitudes and (e, f) longitudinal cross section of soil moisture
averaged over domain latitudes. Location of the Ez Zeraf Game Reserve
permanent wetlands is marked by an arrow. All the times are given in UTC.
Note the UTC+2 h difference to LST in the east.

Figure 8Value of the spatial coupling measure δe calculated
for various afternoon rainfall accumulation times, and averaged over selected
domains, i.e east (6–10∘ N, 24–34∘ E), west
(7–12∘ N, 8∘ W–6∘ E) and north
(14–17∘ N, 7–14∘ E). Locations of the domains are shown
in Fig. 4. Error bars indicate one SD of δe values in every
domain. Note that all times are indicated in UTC.

From Fig. 8 it is seen that the earlier rainfall accumulation time periods,
i.e. 12:00–18:00 UTC in the east and 15:00–21:00 UTC in the west and north, result in
the strongest negative δe difference, and hence spatial SMPC
relationship in all three domains. No positive δe values are found for these time periods, and
the fraction of negative soil moisture gradients preceding rainfall events is
relatively high, i.e. 62, 57 and 55 % for east, west and north
accordingly. Later accumulation times lead to a decrease in the magnitude and
significance of the coupling parameter δe, and an increase
in its spatial variability across the domains. These changes are associated
with an increase in the amount of the positive soil moisture gradients in the
regions (not shown).

Despite the similarities, differences in the SMPC response exist between
the domains. In the east, the spatial SMPC shows the strongest sensitivity
to the rainfall accumulation time and switches the sign to a positive one for
the 18:00–24:00 UTC period. In accordance with Fig. 7d, the 18:00–24:00 UTC
period reflects the afternoon progression of the mature MCS formed during early afternoon
hours at the Ethiopian Highlands and around wetlands. According to Taylor and Lebel (1998) and Taylor et al. (2010), large and
organized MCSs are expected to be more efficient in developing over wetter
soils, associated with a well expressed BL moisture anomaly and higher MSE and
CAPE and, at the same time, might get suppressed over drier surfaces
(Clark et al., 2003). These observations are consistent with the
increase in fraction of positive Δ(Se′Lmax) in
all the domains towards late afternoon hours and the strongest SMPC response
in the east identified here.

Additional analysis reveals that the majority of large and negative soil
moisture gradients in all domains are linked to the rainfall events that are
identified during the first afternoon time step (i.e 12:00 and 15:00 UTC
for the east and west respectively) (not shown), and are therefore likely
linked to weaker MCSs at the early stage of their development. The smaller
and less organized MCSs have shown to be more sensitive to the
thermally induced surface convergence zones and are likely to develop over
spatially drier soils, adjacent to the strong gradients
(Gantner and Kalthoff, 2010). This knowledge is consistent with the strongest
negative δe difference identified here and hence with the
prominent negative SMPC relationship observed during early afternoon times in
all three domains.

5.1 Co-variability of the spatial and temporal SMPC

To further explore the consistency of the identified negative spatial SMPC
with the physical effects we analyse the temporal SMPC relationship. By
analogy to the spatial SMPC, we compute the soil moisture anomaly
Se′Lmax prior to the event and its difference
δe to the typical state. Analysis of
Se′Lmax and its δe indicates a
strong preference for rainfall events to occur over soils that are drier than
their temporal mean (Fig. 9a) and drier than usual
(Fig. 9b). The percentile values Pe lower than 10 %
are found in 67 % of the studied 1∘× 1∘ boxes
(Table 1). This implies that a temporally negative SMPC dominates over
the domain, which reaffirms the co-existence of the negative spatial and
temporal coupling identified by G15, but at a finer 1∘ horizontal
scale.

The question remains as to whether the two coupling relationships are independent of one another. To answer this question we calculate the Spearman rank correlation
coefficient4 event-wise between the soil moisture
anomaly Se′Lmax and soil moisture gradients Δ(Se′Lmax) in every 1∘× 1∘
box.
The correlation map in Fig. 9c shows that a high and significant
correlation exists between Se′Lmax and Δ(Se′Lmax) anywhere in the domain. The mean
correlation of 0.47 over the domain supports the existence of a relatively
strong and positive monotonic relationship between the magnitude of the spatial
soil moisture gradient and the soil moisture anomaly measured in
Lmax.
For comparison, the mean correlation estimated between soil moisture gradients and the mean soil
moisture anomaly over the 1.25∘ event box is small (0.13). All the above suggests that in the North African region the spatial and temporal
SMPC relationships, as defined by the current framework, are not independent of each other.

The strong and positive correlation (in time) identified between the soil
moisture anomalies and gradients also yields a regional co-variability of the
SMPC patterns. The spatial correlation between the two coupling distributions
is high (0.64). The largest magnitudes of both Se′Lmax and
Δ(Se′Lmax) parameters and their corresponding
δe measures are found in the southern part of the domain. These
regions are generally characterized as the areas of higher BL moisture and rainfall
frequency, and therefore higher variability of soil moisture in time and space.

Mechanistically, the presence of the temporally negative SMPC in the areas of
the highest BL moisture in the domain (or lowest lifting condensation level
(LCL); Fig. 10a) is consistent with a higher relevance of
mechanisms associated with the BL growth for convection initialization in
regions of higher CAPE and lower convective inhibition (CIN)
(Adler et al., 2011; Gantner and Kalthoff, 2010; Klüpfel et al., 2011). In this way, larger negative
deviations of the soil moisture amount from its climatological mean, i.e.
δe, would lead to thermals that are stronger than usual, which can
more easily overcome CIN and release CAPE (Klüpfel et al., 2011).

Moreover, in combination with strong negative spatial gradients, these
strong thermals can initiate breeze-like circulations, creating more
favourable conditions for bringing the BL up to the LFC, especially over the
southern regions, where BL moisture is in abundance. The relevance of drier
surface conditions for moist convection development on event days over the
wetter latitudes is supported by the observed significant increase of the LCL
height (decrease in pressure) in the south on event days compared to the
typical state (Fig. 10b, red shading). The higher LCL is associated
with a decrease of BL relative and specific humidity (not shown) and supports
the relevance of drier surface conditions for convection intensification as
opposed to variations in BL water vapour amount prior to the events.

A different picture is observed over the drier latitudes of northern Sahel at
the Sahara margin. There, the pre-event LCLs are found to be significantly lower (higher pressure)
compared to its typical (climatological) state. This suggests a higher amount of water vapor
in the BL (not shown) on the event days over the dry regions (Fig. 10b, blue shading). This result is consistent with
the previously reported decisive role of low-level moisture on MCS evolution
in the drier Sahelian regions (Klüpfel et al., 2012). At these latitudes the
northward excursion of moist monsoon air has been shown to favour convective
activity (Barthe et al., 2010; Cuesta et al., 2010).

Considering also the relatively large number of dry days (10 days on average)
preceding rain events in the north, it is less likely that underlying surface
heterogeneity caused by a previous rainfall could have an influence on
convection development on the event day. In the case study of
Klüpfel et al. (2012) an MCS was initiated due to the arrival of the cold pool and
convergence zone emanated by a remote convective system hundreds of kilometres
away. Similar mechanisms may play a role in moist convection development in
northern Sahel.

Figure 11Conceptual diagram, illustrating intensification of moist convection
by the initiated “breeze-like” circulations under favourable conditions of
co-existing negative spatial and negative temporal SMPC effects. On the one
hand, typically observed 2- to 4-day periodicity of rainfall in western Africa
leads to a strong drying of the upper soil layer in the location A prior to
the rain, and therefore increases sensible heat and buoyancy flux locally.
Simultaneously, recent rainfall in B produces wet soils – a potential
moisture supply area for the location A. Strong spatial gradients in soil
moisture between locations A and B together with a relatively strong buoyancy
flux in A can favour formation of thermally induced circulations under benign
wind conditions. Considering a relatively weak mean surface wind of
2–3 m s−1 observed prior to the rainfall events over southern
latitudes, the mesoscale circulations are likely to be initiated.

Figure 12Conceptual diagrams of the relationship between daily rainfall
occurrence and the surface moisture variability associated to it, in time, as
representative of (a) West Africa and temporally negative SMPC and
(b) central and northern Europe and positive temporal SMPC.

6.1 Role of thermally induced circulations in moist convection development

The dominant negative spatial SMPC relationship observed over the North
African region agrees on the sign of the SMPC suggested by previous case studies and
modelling studies (e.g. Birch et al., 2013; Froidevaux et al., 2014; Taylor and Ellis, 2006), and
reconciles a number of physical effects. Following the sensitivity analyses,
the two main factors are identified, which directly influence the magnitude
and significance of the negative spatial SMPC relationship. These are the
time of the afternoon rainfall accumulation and the extreme soil moisture
gradients. Our results show that the observed relationship between the
spatial SMPC measure and the time of rainfall accumulation conforms well with
the varying sensitivity of rainfall to the underlying soil moisture
conditions for different stages of MCS development (Alonge et al., 2007; Clark et al., 2003; Taylor and Ellis, 2006; Taylor and Lebel, 1998; Taylor et al., 2010). This further suggests
potentially higher relevance of drier soils and soil moisture heterogeneity
for rainfall when the rain systems are smaller and at an early stage of their
development, consistent with results of e.g. Gantner and Kalthoff (2010). All the
above also emphasizes the importance of considering the MCS properties as an
additional factor in the analysis of the SMPC (Ford et al., 2015).

Extreme soil moisture gradients are found to predefine the significance of
the negative spatial coupling in 30 % of the domain grid boxes.
Concurrently, the extremes tend to cluster in the direct vicinity of major
flooded areas and the irrigated land. The identified sensitivity of the
afternoon rainfall to the strong soil moisture gradients adjacent to wetland
areas shows consistency with the role of a wetland breeze mechanism in
convection intensification over spatially drier soils. If this is true, our
results demonstrate for the first time that the wetland breeze mechanism
could be a systematic feature in the North African region, with further
implications for the rainfall predictability.

This observed sensitivity of the SMPC measure to the flooded areas and to the
MCS life cycle complies well with the potential role of thermally induced
circulations in afternoon rainfall development over strong negative soil
moisture gradients. Moreover, the identified preference of rainfall to occur
simultaneously over temporally drier soils (negative temporal SMPC) and
strong negative soil moisture gradients (negative spatial SMPC) might be
considered as the most effective combination to initiate thermal
circulations. This would imply a presence of a higher buoyancy and moisture
flux at the event location, and hence a higher probability of convection
development. Schematic representation of the deep convection initiation by
the breeze-like circulations formed under the co-existence of the two SMPC
effects is illustrated in Fig. 11.

6.2 Role of rainfall persistence

In the context of this study, the drying of the soil prior to the rainfall
events might be considered as the primary process that underlies the
magnitude of both SMPC relationships, and helps to explain the opposite sign
of the temporal coupling identified in the North African region as compared
to the temperate latitudes and wet climates (G15).

Consistent with the observed 2- to 4-day periodicity of rainfall in Western
Africa (Laing et al., 2012; Taylor and Lebel, 1998), 2 to 3 dry days (rain < 1 mm) on
average are found to precede each convective event day over southern
latitudes, suggesting
a strong drying of the upper soil layer in the event locations prior to the rain. The number of dry days reaches 10 over the dry and deserted regions in the
north. Following the analysis of Schwendike et al. (2010), an almost complete
recovery of the pre-rainfall surface moisture conditions may be expected in
2–3 days following the rainfall. Schematically, this typical variability of
rainfall and soil moisture might be illustrated as a sequence of daily rain
events separated by the periods of drying (Fig. 12a). From the figure
it is seen that prior to the rain events the soil dries out, and soil
moisture reaches a certain minimum value Smin. The climatology
value Sclim of soil moisture in the same location, however, is
expected to be higher than any Smin in most of the cases, as it
includes all dry and wet event days. Hence, when subtracted from the
climatological value, a soil moisture value measured prior to the event will very
likely yield a negative anomaly – Se′Lmax,
especially when averaged over many events. Therefore, a negative correlation
between the soil moisture anomaly and rainfall might be expected. Though
discussed in the framework of North Africa, similar behaviour might be
expected in other water-limited regions of the world.

A different situation might occur in the wet temperate latitudes, where the variability
of rainfall is to a large extent linked to fluctuations between the passage of a cyclone
and a blocking situation (Schär et al., 1999). Such a behaviour might be illustrated as
a multi-day sequence of rain events, associated with precipitation persistence as
defined by the persistence in the weather regimes (Fig. 12b; see also Fig. 2
in Hohenegger et al., 2009). During these periods soil moisture increases and remains
relatively high. Hence, a higher fraction of events might be expected to occur over
soils that are wetter than usual, resulting in a positive soil moisture anomaly
Se′Lmax prior to the event. The above relationship is consistent
with the positive temporal SMPC, identified in G15. It is important to note, however, that the rainfall persistence may not be
solely atmospherically driven, but may also reflect effects of the land
surface (Guillod et al., 2014; Salvucci et al., 2002; Seneviratne et al., 2010).

The modulation of the SMPC sign depending on the large-scale weather regime
was studied e.g. by Boé (2012) over France. The analysis showed that the
synoptic blocking situations generally associated with drier conditions lead
to a negative SMPC, while a positive correlation of rainfall to drier soil
conditions was observed in a wet weather regime. Similarly, the most pronounced
effect of negative soil moisture gradients on convection initiation over
Europe and a higher correlation of the gradients to land surface temperatures
was observed for the period with less antecedent rainfall (Taylor, 2015).

In this study, we revisit the negative spatial and negative temporal SMPC
relationships identified earlier by T12 and G15 on the
5∘× 5∘ horizontal grid. We use the
probability-based approach of T12 and 10 years of satellite-based soil
moisture and precipitation data (i) to identify the potential link of the
observed statistical relationships to the physical mechanisms and (ii) to
study the regional co-variability of the SMPC effects.

We find that the negative spatial coupling dominates over the region of
North Africa. The result is independent of the choice of the observational
data sets and is robust with respect to the event aggregation (spatial) scale.
Compared to the coarser 5∘× 5∘ grid previously used,
the appeal of the finest considered 1∘× 1∘ event-aggregation
scale is that it reveals links to the wetland areas and rivers which can not be captured at
the coarser scale.

The co-variability analysis of the two SMPC relationships indicates that
spatial and temporal effects of soil moisture on afternoon precipitation in
the North African region do not only co-exist but are also dependent on one
another. The latter suggests that if rain falls over temporally drier soils,
it is likely to be surrounded by a wetter environment. This combination is
consistent with the relevance of processes associated with the dominance of
the sensible heat flux and boundary layer growth on convection initiation, and
supports the role of mesoscale variability in surface soil moisture for deep
convection development.

The identified negative sign of the temporal coupling in the semi-arid
conditions of the Sahelian environment is not unexpected. The drying of the
soil for several days prior to the rainfall events is likely to underlay the
preference of rain to occur over temporally drier soils. This additionally
may play a role in the opposite sign of the temporal coupling in the North
African region as compared to the positive relationship identified in wetter
climates by G15. For the same reason, the predictability potential of the
temporal effect on rainfall in the North African region is expected to be
lower compared to the spatial effect.

Analysis of the spatial SMPC measure and factors which can influence its
magnitude and variability in particular reveals two hot spot regions,
where predictability skill of spatial soil moisture variability on rainfall
might be higher. These are the western African domain (7–15∘ N,
10∘ W–7∘ E) and South Sudan in the east
(5–13∘ N, 24–34∘ E). In the Western domain, the negative
spatial SMPC signal is indicated to be more robust. In the east, the spatial
coupling is found to be largely modulated by the presence of wetlands and is
susceptible to the number of longer lived propagating MCSs. The analysis of
the BL moisture conditions (here, LCL) preceding the rainfall events further
supports the potential relevance of spatially and temporally drier soils for
convection development in the south of the domain, where BL moisture is in
abundance. In the drier northern latitudes the variability of BL moisture,
associated with intrusions of moisture from the south, seems to be more important.

Following our analysis, a number of potential improvements to the framework
might be summarized. Apparent non-local effects of water bodies, which are
originally excluded by the method, hint towards the potential gaps in the
filtering procedure and emphasize the potential role of moist convection
evolution and propagation that are neglected by the method. The presence of
wetland regions themselves, as we have shown, complicates the interpretation of the
SMPC relationships. The uncertainty estimates of the soil moisture parameter
derived over the recursively flooded regions are still missing. In the
future, dynamical wetland products like the Global Inundation Extent from
Multi-Satellites (GIEMS, Prigent et al., 2007, 2012) may be used to
better isolate the effect of water bodies on moist convection development.

Notwithstanding these limitations, the present study demonstrates the ability
of probability-based methods to identify characteristic features of physical
effects. Considering continuous increase in the availability, time span and
quality of satellite data, development of similar statistical methods should
be valuable. The region of the strong negative SMPC identified in the east
would highly benefit from more modelling and observational analysis in the
eastern Sahel. The knowledge on the regional variability of the SMPC
presented here can be further explored in drought and climate change
research and observational campaigns and used for the validation of global climate models.

A1 Method used for identification of potentially flooded locations

The following paragraph describes the methodology used to identify events in which
Lmin locations are likely flooded. Following this methodology, a
climatology of soil moisture drying rates is computed in every
Lmin location first. Climatology is calculated from values
measured in the same Lmin location, in the same month as the
event but during the non-event years. Soil moisture drying rate is computed
as the difference between soil moisture at 13:30 LST on the event day 0 and
the previous day −1. Days with non-zero precipitation between two soil
moisture measurements are excluded.

As a next step, we compute a climatology of soil moisture values on the day −1 to
estimate a potential range of soil moisture conditions in every
Lmin location. Finally, for every Lmin location we
identify a linear regression function which fits best into a scatter plot
relationship between drying rates and initial soil moisture values. Based on
the slope of the linear regression and a climatological range of drying rates
we stratify Lmin locations as being potentially “always dry”,
“normal” or “always wet” (Fig. A1). Locations which are potentially
always dry may be representative of rocky sand areas and hence will most
often show low soil moisture values and drying rates close to zero. The cases
of soil being always wet should indicate mostly high soil moisture values, but at
the same time small drying rates, on the condition that a water supply is
present. The “normal” case is expected to show a clear relationship between
the drying rate and initial soil moisture content. The higher the soil
moisture is, the larger the drying rate is expected to be.

Figure A1Examples of
three possible relationships between climatology of soil moisture drying rate
and absolute soil moisture in Lmin locations. The red numbers
indicate values of a slope and Spearman correlation respectively.

Because the identification procedure requires selection of thresholds, the
distribution of the potentially flooded locations will slightly vary
depending on the selected threshold of the regression slope, minimum drying
rate value or minimum absolute soil moisture. For the calculation of the
potential wetland locations presented in Fig. 6c we chose two
thresholds: the slope had to be larger than −0.15 and the soil moisture
values had to be larger than 20 %.

Table A1Percentage of 5∘× 5∘ grid boxes with
significantly negative (Pe< 10 %) and positive
(Pe> 90 %) spatial SMPC over the North African domain in
this study and previous studies of T12 and G15. Different data set
combinations used in T12 and G15 are listed. “Merged” represents an
integrated product composed of grid boxes in which either the AMSR-E or the ASCAT
soil moisture data set was identified to be the best following a
quality-control check. Following visual inspection, the experiments in which
a significant negative SMPC relationship exists in the western region of the
Sahelian domain are indicated with a * sign. For more details on
various data set combinations, the reader is referred to the original
papers.

The authors would like to thank the Max Planck Institute for Meteorology (MPI-M)
and the International Max Planck Research School (IMPRS) for providing
facilities, material and scientific support which made publication of this
paper possible. The authors would like to acknowledge Christopher Taylor,
Benoît Guillod and Alexander Mahura for helpful comments on the study, and
Stephan Kern for data support. We also thank George Huffman and Robert
Parinussa for their clarifications related to TMPA and AMSR-E data products
respectively.

The article processing charges for this open-access publication were covered by the Max Planck Society.

Nicholson, S. E.: The West African Sahel: A review of recent studies on the
rainfall regime and its interannual variability, ISRN Meteorology, 2013,
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Following T12, a box size
of 1.25∘× 1.25∘ is selected as minimum possible
size to resolve soil moisture variability around the centre of the box,
taking into account the 50 km footprint of the AMSR-E soil moisture.

Spearman correlation is a measure of the monotonic
relationship. Therefore, a zero or low correlation value does not imply a zero
relationship between two variables.

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In North Africa rain storms can be as vital as they are devastating. The present study uses multi-year satellite data to better understand how and where soil moisture conditions affect development of rainfall in the area. Our results reveal two major regions in the southwest and southeast, where drier soils show higher potential to cause rainfall development. This knowledge is essential for the hydrological sector, and can be further used by models to improve prediction of rainfall and droughts.

In North Africa rain storms can be as vital as they are devastating. The present study uses...